





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      lowercase_         =						"""platform"""

      import jax
      import jax.numpy as jnp

      from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          shift_tokens_right,
      )




def      __UpperCamelCase    (_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		)							->  Union[str, Any]:
  if attention_mask is None:
    lowercase__           =    np.where(input_ids != config.pad_token_id						,		1						,		0						)
  if decoder_attention_mask is None:
    lowercase__           =    np.where(decoder_input_ids != config.pad_token_id						,		1						,		0						)
  if head_mask is None:
    lowercase__           =    np.ones((config.encoder_layers, config.encoder_attention_heads)						)
  if decoder_head_mask is None:
    lowercase__           =    np.ones((config.decoder_layers, config.decoder_attention_heads)						)
  if cross_attn_head_mask is None:
    lowercase__           =    np.ones((config.decoder_layers, config.decoder_attention_heads)						)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }
class 							SCREAMING_SNAKE_CASE  :


   def __init__(       self     :							List[str]     ,	a     :							str     ,	a     :							Any=13     ,	a     :							List[str]=7     ,	a     :							Any=True     ,	a     :							str=False     ,	a     :							List[Any]=99     ,	a     :							List[Any]=16     ,	a     :							Any=2     ,	a     :							Dict=4     ,	a     :							List[str]=4     ,	a     :							int="gelu"     ,	a     :							int=0.1     ,	a     :							List[Any]=0.1     ,	a     :							str=32     ,	a     :							int=2     ,	a     :							Tuple=1     ,	a     :							List[str]=0     ,	a     :							Optional[Any]=0.02     ,	)->  List[str]:





     """simple docstring"""







     lowercase__           =    parent
     lowercase__           =    batch_size
     lowercase__           =    seq_length
     lowercase__           =    is_training
     lowercase__           =    use_labels
     lowercase__           =    vocab_size
     lowercase__           =    hidden_size
     lowercase__           =    num_hidden_layers
     lowercase__           =    num_attention_heads
     lowercase__           =    intermediate_size
     lowercase__           =    hidden_act
     lowercase__           =    hidden_dropout_prob
     lowercase__           =    attention_probs_dropout_prob
     lowercase__           =    max_position_embeddings
     lowercase__           =    eos_token_id
     lowercase__           =    pad_token_id
     lowercase__           =    bos_token_id
     lowercase__           =    initializer_range


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Tuple			)->  Tuple:





     """simple docstring"""







     lowercase__           =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,	self.vocab_size			)     ,	3     ,	self.vocab_size			)
     lowercase__           =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,	dtype=np.intaa			))     ,	-1			)

     lowercase__           =    shift_tokens_right(a     ,	1     ,	2			)

     lowercase__           =    BlenderbotSmallConfig(
         vocab_size=self.vocab_size     ,	d_model=self.hidden_size     ,	encoder_layers=self.num_hidden_layers     ,	decoder_layers=self.num_hidden_layers     ,	encoder_attention_heads=self.num_attention_heads     ,	decoder_attention_heads=self.num_attention_heads     ,	encoder_ffn_dim=self.intermediate_size     ,	decoder_ffn_dim=self.intermediate_size     ,	dropout=self.hidden_dropout_prob     ,	attention_dropout=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	eos_token_id=self.eos_token_id     ,	bos_token_id=self.bos_token_id     ,	pad_token_id=self.pad_token_id     ,	initializer_range=self.initializer_range     ,	use_cache=a     ,	)
     lowercase__           =    prepare_blenderbot_inputs_dict(a     ,	a     ,	a			)
     return config, inputs_dict


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[int]			)->  List[Any]:





     """simple docstring"""







     lowercase__     ,       lowercase__           =    self.prepare_config_and_inputs()
     return config, inputs_dict


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Any     ,	a     :							List[Any]     ,	a     :							int     ,	a     :							str			)->  Optional[int]:





     """simple docstring"""







     lowercase__           =    20
     lowercase__           =    model_class_name(a			)

     lowercase__           =    model.encode(inputs_dict['input_ids']			)

     lowercase__     ,       lowercase__           =    (
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     lowercase__           =    model.init_cache(decoder_input_ids.shape[0]     ,	a     ,	a			)
     lowercase__           =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,	dtype='i4'			)

     lowercase__           =    jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]     ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,	)
     lowercase__           =    model.decode(
         decoder_input_ids[:, :-1]     ,	a     ,	decoder_attention_mask=a     ,	past_key_values=a     ,	decoder_position_ids=a     ,	)

     lowercase__           =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,	dtype='i4'			)
     lowercase__           =    model.decode(
         decoder_input_ids[:, -1:]     ,	a     ,	decoder_attention_mask=a     ,	past_key_values=outputs_cache.past_key_values     ,	decoder_position_ids=a     ,	)

     lowercase__           =    model.decode(a     ,	a			)

     lowercase__           =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
     self.parent.assertTrue(diff < 1E-3     ,	msg=f"""Max diff is {diff}"""			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Any     ,	a     :							Dict     ,	a     :							Tuple     ,	a     :							Tuple			)->  Union[str, Any]:





     """simple docstring"""







     lowercase__           =    20
     lowercase__           =    model_class_name(a			)

     lowercase__           =    model.encode(inputs_dict['input_ids']			)

     lowercase__     ,       lowercase__           =    (
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     lowercase__           =    jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
         ]     ,	axis=-1     ,	)

     lowercase__           =    model.init_cache(decoder_input_ids.shape[0]     ,	a     ,	a			)
     lowercase__           =    jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]     ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,	)

     lowercase__           =    model.decode(
         decoder_input_ids[:, :-1]     ,	a     ,	decoder_attention_mask=a     ,	past_key_values=a     ,	decoder_position_ids=a     ,	)
     lowercase__           =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,	dtype='i4'			)
     lowercase__           =    model.decode(
         decoder_input_ids[:, -1:]     ,	a     ,	past_key_values=outputs_cache.past_key_values     ,	decoder_attention_mask=a     ,	decoder_position_ids=a     ,	)

     lowercase__           =    model.decode(a     ,	a     ,	decoder_attention_mask=a			)

     lowercase__           =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
     self.parent.assertTrue(diff < 1E-3     ,	msg=f"""Max diff is {diff}"""			)






@require_flax
class 							SCREAMING_SNAKE_CASE  (unittest.TestCase ):
   _UpperCamelCase       :						Dict					   =			99


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Any			)->  Dict:





     """simple docstring"""







     lowercase__           =    np.array(
         [
             [71, 82, 18, 33, 46, 91, 2],
             [68, 34, 26, 58, 30, 82, 2],
             [5, 97, 17, 39, 94, 40, 2],
             [76, 83, 94, 25, 70, 78, 2],
             [87, 59, 41, 35, 48, 66, 2],
             [55, 13, 16, 58, 5, 2, 1],  # note padding
             [64, 27, 31, 51, 12, 75, 2],
             [52, 64, 86, 17, 83, 39, 2],
             [48, 61, 9, 24, 71, 82, 2],
             [26, 1, 60, 48, 22, 13, 2],
             [21, 5, 62, 28, 14, 76, 2],
             [45, 98, 37, 86, 59, 48, 2],
             [70, 70, 50, 9, 28, 0, 2],
         ]     ,	dtype=np.intaa     ,	)

     lowercase__           =    input_ids.shape[0]
     lowercase__           =    BlenderbotSmallConfig(
         vocab_size=self.vocab_size     ,	d_model=24     ,	encoder_layers=2     ,	decoder_layers=2     ,	encoder_attention_heads=2     ,	decoder_attention_heads=2     ,	encoder_ffn_dim=32     ,	decoder_ffn_dim=32     ,	max_position_embeddings=48     ,	eos_token_id=2     ,	pad_token_id=1     ,	bos_token_id=0     ,	)
     return config, input_ids, batch_size


   def 			SCREAMING_SNAKE_CASE_				(       self     :							int			)->  int:





     """simple docstring"""







     lowercase__     ,       lowercase__     ,       lowercase__           =    self._get_config_and_data()
     lowercase__           =    FlaxBlenderbotSmallForConditionalGeneration(a			)
     lowercase__           =    lm_model(input_ids=a			)
     lowercase__           =    (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['logits'].shape     ,	a			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Optional[int]			)->  Tuple:





     """simple docstring"""







     lowercase__           =    BlenderbotSmallConfig(
         vocab_size=self.vocab_size     ,	d_model=14     ,	encoder_layers=2     ,	decoder_layers=2     ,	encoder_attention_heads=2     ,	decoder_attention_heads=2     ,	encoder_ffn_dim=8     ,	decoder_ffn_dim=8     ,	max_position_embeddings=48     ,	)
     lowercase__           =    FlaxBlenderbotSmallForConditionalGeneration(a			)
     lowercase__           =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,	dtype=np.intaa			)
     lowercase__           =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,	dtype=np.intaa			)
     lowercase__           =    lm_model(input_ids=a     ,	decoder_input_ids=a			)
     lowercase__           =    (*summary.shape, config.vocab_size)
     self.assertEqual(outputs['logits'].shape     ,	a			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Tuple			)->  Optional[Any]:





     """simple docstring"""







     lowercase__           =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,	dtype=np.intaa			)
     lowercase__           =    shift_tokens_right(a     ,	1     ,	2			)
     lowercase__           =    np.equal(a     ,	1			).astype(np.floataa			).sum()
     lowercase__           =    np.equal(a     ,	1			).astype(np.floataa			).sum()
     self.assertEqual(shifted.shape     ,	input_ids.shape			)
     self.assertEqual(a     ,	n_pad_before - 1			)
     self.assertTrue(np.equal(shifted[:, 0]     ,	2			).all()			)






@require_flax
class 							SCREAMING_SNAKE_CASE  (snake_case_     ,      unittest.TestCase     ,      snake_case_ ):
   _UpperCamelCase       :						Any					   =			True
   _UpperCamelCase       :						int					   =			(
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   _UpperCamelCase       :						str					   =			(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


   def 			SCREAMING_SNAKE_CASE_				(       self     :							str			)->  Union[str, Any]:





     """simple docstring"""







     lowercase__           =    FlaxBlenderbotSmallModelTester(self			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[Any]			)->  List[Any]:





     """simple docstring"""







     lowercase__     ,       lowercase__           =    self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(a     ,	a     ,	a			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							List[str]			)->  Optional[Any]:





     """simple docstring"""







     lowercase__     ,       lowercase__           =    self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(a     ,	a     ,	a			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							Tuple			)->  Dict:





     """simple docstring"""







     lowercase__     ,       lowercase__           =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__			):
         lowercase__           =    self._prepare_for_class(a     ,	a			)
         lowercase__           =    model_class(a			)

         @jax.jit
         def encode_jitted(a     :							str     ,	a     :							Optional[Any]=None     ,	**a     :							Any			):
           return model.encode(input_ids=a     ,	attention_mask=a			)

         with self.subTest('JIT Enabled'			):
           lowercase__           =    encode_jitted(**a			).to_tuple()

         with self.subTest('JIT Disabled'			):
           with jax.disable_jit():
             lowercase__           =    encode_jitted(**a			).to_tuple()

         self.assertEqual(len(a			)     ,	len(a			)			)
         for jitted_output, output in zip(a     ,	a			):
           self.assertEqual(jitted_output.shape     ,	output.shape			)


   def 			SCREAMING_SNAKE_CASE_				(       self     :							int			)->  Optional[Any]:





     """simple docstring"""







     lowercase__     ,       lowercase__           =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__			):
         lowercase__           =    model_class(a			)
         lowercase__           =    model.encode(inputs_dict['input_ids']     ,	inputs_dict['attention_mask']			)

         lowercase__           =    {
             'decoder_input_ids': inputs_dict['decoder_input_ids'],
             'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
             'encoder_outputs': encoder_outputs,
         }

         @jax.jit
         def decode_jitted(a     :							List[str]     ,	a     :							List[str]     ,	a     :							Union[str, Any]			):
           return model.decode(
               decoder_input_ids=a     ,	decoder_attention_mask=a     ,	encoder_outputs=a     ,	)

         with self.subTest('JIT Enabled'			):
           lowercase__           =    decode_jitted(**a			).to_tuple()

         with self.subTest('JIT Disabled'			):
           with jax.disable_jit():
             lowercase__           =    decode_jitted(**a			).to_tuple()

         self.assertEqual(len(a			)     ,	len(a			)			)
         for jitted_output, output in zip(a     ,	a			):
           self.assertEqual(jitted_output.shape     ,	output.shape			)


   @slow
   def 			SCREAMING_SNAKE_CASE_				(       self     :							Union[str, Any]			)->  List[str]:





     """simple docstring"""







     for model_class_name in self.all_model_classes:
       lowercase__           =    model_class_name.from_pretrained('facebook/blenderbot_small-90M'			)
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       lowercase__           =    np.ones((1, 1)			) * model.config.eos_token_id
       lowercase__           =    model(a			)
       self.assertIsNotNone(a			)



"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
